


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    BertTokenizerFast,
						    BlipImageProcessor,
						    GPTaTokenizer,
						    InstructBlipProcessor,
						    PreTrainedTokenizerFast,
						)







@require_vision
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							tempfile.mkdtemp()

						UpperCamelCase__       =							BlipImageProcessor()
						UpperCamelCase__       =							GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"  )
						UpperCamelCase__       =							BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"  )

						UpperCamelCase__       =							InstructBlipProcessor(snake_case   ,    snake_case   ,    snake_case  )

						processor.save_pretrained(self.tmpdirname  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return AutoProcessor.from_pretrained(self.tmpdirname   ,    **snake_case  ).tokenizer




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return AutoProcessor.from_pretrained(self.tmpdirname   ,    **snake_case  ).image_processor




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return AutoProcessor.from_pretrained(self.tmpdirname   ,    **snake_case  ).qformer_tokenizer




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						shutil.rmtree(self.tmpdirname  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[np.random.randint(255   ,    size=(3, 30, 400)   ,    dtype=np.uinta  )]

						UpperCamelCase__       =							[Image.fromarray(np.moveaxis(snake_case   ,    0   ,    -1  )  ) for x in image_inputs]

						return image_inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=self.get_tokenizer()   ,    image_processor=self.get_image_processor()   ,    qformer_tokenizer=self.get_qformer_tokenizer()   ,    )
						processor.save_pretrained(self.tmpdirname  )

						UpperCamelCase__       =							self.get_tokenizer(bos_token="(BOS)"   ,    eos_token="(EOS)"  )
						UpperCamelCase__       =							self.get_image_processor(do_normalize=snake_case   ,    padding_value=1.0  )

						UpperCamelCase__       =							InstructBlipProcessor.from_pretrained(
						    self.tmpdirname   ,    bos_token="(BOS)"   ,    eos_token="(EOS)"   ,    do_normalize=snake_case   ,    padding_value=1.0  )

						self.assertEqual(processor.tokenizer.get_vocab()   ,    tokenizer_add_kwargs.get_vocab()  )
						self.assertIsInstance(processor.tokenizer   ,    snake_case  )

						self.assertEqual(processor.image_processor.to_json_string()   ,    image_processor_add_kwargs.to_json_string()  )
						self.assertIsInstance(processor.image_processor   ,    snake_case  )
						self.assertIsInstance(processor.qformer_tokenizer   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_qformer_tokenizer()

						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=snake_case   ,    image_processor=snake_case   ,    qformer_tokenizer=snake_case  )

						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							image_processor(snake_case   ,    return_tensors="np"  )
						UpperCamelCase__       =							processor(images=snake_case   ,    return_tensors="np"  )

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()   ,    input_processor[key].sum()   ,    delta=1E-2  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_qformer_tokenizer()

						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=snake_case   ,    image_processor=snake_case   ,    qformer_tokenizer=snake_case  )

						UpperCamelCase__       =							"lower newer"

						UpperCamelCase__       =							processor(text=snake_case  )

						UpperCamelCase__       =							tokenizer(snake_case   ,    return_token_type_ids=snake_case  )
						UpperCamelCase__       =							qformer_tokenizer(snake_case   ,    return_token_type_ids=snake_case  )

						for key in encoded_tokens.keys():
									self.assertListEqual(encoded_tokens[key]   ,    encoded_processor[key]  )

						for key in encoded_tokens_qformer.keys():
									self.assertListEqual(encoded_tokens_qformer[key]   ,    encoded_processor["qformer_" + key]  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_qformer_tokenizer()

						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=snake_case   ,    image_processor=snake_case   ,    qformer_tokenizer=snake_case  )

						UpperCamelCase__       =							"lower newer"
						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							processor(text=snake_case   ,    images=snake_case  )

						self.assertListEqual(
						    list(inputs.keys()  )   ,    ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]   ,    )

						# test if it raises when no input is passed
						with pytest.raises(snake_case  ):
									processor()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_qformer_tokenizer()

						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=snake_case   ,    image_processor=snake_case   ,    qformer_tokenizer=snake_case  )

						UpperCamelCase__       =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase__       =							processor.batch_decode(snake_case  )
						UpperCamelCase__       =							tokenizer.batch_decode(snake_case  )

						self.assertListEqual(snake_case   ,    snake_case  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_qformer_tokenizer()

						UpperCamelCase__       =							InstructBlipProcessor(
						    tokenizer=snake_case   ,    image_processor=snake_case   ,    qformer_tokenizer=snake_case  )

						UpperCamelCase__       =							"lower newer"
						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							processor(text=snake_case   ,    images=snake_case  )

						self.assertListEqual(
						    list(inputs.keys()  )   ,    ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]   ,    )






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		(DDIMParallelScheduler,)
			_UpperCamelCase		:       List[Any] 			=		(('eta', 0.0), ('num_inference_steps', 50))




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(**snake_case  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter

						scheduler.set_timesteps(snake_case  )

						for t in scheduler.timesteps:
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						return sample




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=snake_case  )

						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(steps_offset=1  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )
						scheduler.set_timesteps(5  )
						assert torch.equal(scheduler.timesteps   ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,    [0.002, 0.02, 0.2, 2]  ):
									self.check_over_configs(beta_start=snake_case   ,    beta_end=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.check_over_configs(thresholding=snake_case  )
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=snake_case   ,    prediction_type=snake_case   ,    sample_max_value=snake_case   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t in [1, 10, 49]:
									self.check_over_forward(time_step=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, num_inference_steps in zip([1, 10, 50]   ,    [10, 50, 500]  ):
									self.check_over_forward(time_step=snake_case   ,    num_inference_steps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, eta in zip([1, 10, 49]   ,    [0.0, 0.5, 1.0]  ):
									self.check_over_forward(time_step=snake_case   ,    eta=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420   ,    400  ) - 0.14771  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980   ,    960  ) - 0.32460  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487   ,    486  ) - 0.00979  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999   ,    998  ) - 0.02  )  ) < 1E-5




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0
						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter
						UpperCamelCase__       =							self.dummy_sample_deter + 0.1
						UpperCamelCase__       =							self.dummy_sample_deter - 0.1

						UpperCamelCase__       =							samplea.shape[0]
						UpperCamelCase__       =							torch.stack([samplea, samplea, samplea]   ,    dim=0  )
						UpperCamelCase__       =							torch.arange(snake_case  )[0:3, None].repeat(1   ,    snake_case  )

						UpperCamelCase__       =							model(samples.flatten(0   ,    1  )   ,    timesteps.flatten(0   ,    1  )  )
						UpperCamelCase__       =							scheduler.batch_step_no_noise(snake_case   ,    timesteps.flatten(0   ,    1  )   ,    samples.flatten(0   ,    1  )   ,    snake_case  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 1147.7904  ) < 1E-2
						assert abs(result_mean.item() - 0.4982  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop()

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 172.0067  ) < 1E-2
						assert abs(result_mean.item() - 0.223967  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(prediction_type="v_prediction"  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 52.5302  ) < 1E-2
						assert abs(result_mean.item() - 0.0684  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.8295  ) < 1E-2
						assert abs(result_mean.item() - 0.1951  ) < 1E-3







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.0784  ) < 1E-2
						assert abs(result_mean.item() - 0.1941  ) < 1E-3


